Improving your game performance with data visualization

On: May 31, 2013
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About Ferdy Looijen
I am an alumni New Media & Digital Culture master student from the University of Amsterdam. Before this master program I completed the Bachelor of Arts degree at the University of Amsterdam, and the Bachelor of Communication and Multimedia Design at the Hogeschool van Amsterdam. My BA thesis focussed on the Professionalization of the Modding Culture. Furthermore, my MA thesis, 'Gamification of Gaming', showed how digital distribution platforms make use of gamification elements in order to keep their users engaged. I am interested in games, social media and marketing. For more information you could have a look at my LinkedIn profile: Ferdy Looijen


Nowadays, more and more game producers release video games on digital distribution platforms. Besides the increase in digital distribution of game content, these platforms and game developers offer gamers an experience out of a gameplay environment. For instance, these platforms have integrated other gaming services such as new communication features, achievements, reward systems, and gameplay statistics. Some of these changes are recognizable in social platforms such as Battlelog and Xbox Live, which not only allow players to purchase game content digitally, but also allow players to analyze their gameplay performance and communicate within a social network. Battlelog is an innovative social platform that is produced by Electronic Arts (EA), and the Xbox Live platform is part of the Microsoft Xbox360 which is mainly designed for console games. Both Battlelog and Xbox Live offer gamers all sorts of stats tracking and visualizations of their gameplay data and achievements. Furthermore, these platforms allow players to analyze and discuss their game data with other gamers. More interestingly is that leaderboards, achievements, badges, and other forms of visible progress are essential components on these platforms. Moreover, it seems that the visualization of game data on these platforms has become a significant complement to gaming nowadays. For instance, it provides players a casual approach of data analysis in order to gain more insight in their own performance.

This can be recognized in the work of Ben Medler, which concentrates on achievements and game data visualization. He examined how visualization, analytics and games intersect. Moreover, he argues that “play analytic systems surround the experience of playing a game, visualizing data collected from players and act as external online hubs where players congregate” (Medler 14). In this respect, platforms such as Xbox Live and Battelog can also be seen as play analytic systems. Medler further explains this term by saying that play analytic systems “allow a player’s data and the data from other players to be combined and analyzed outside of gameplay” (14). An example of such an analytic system is a high score leaderboard. A leaderboard is a game mechanic which is essential for making comparisons between players. Medler explains that leaderboards are often available outside the game on websites or platforms. In this way, players can monitor their scores outside the game and discuss this with friends. In addition, general game stats and stored collected achievements can also be viewed and analyzed outside the actual gameplay.

Figure 1: Stats overview (screenshot taken from Battelog interface)


When looking closely at the social platform Battlelog, it becomes clear that players are able to track and analyze their game stats from their web browser. This platform is an essential part of the core of a game, such as Battlefield 3 and Medal of Honor. It moved many traditional in-game features to the platform, such as player profiles, statistics, leaderboards, and several other things that are associated with a player’s game progress. Moreover, these stored statistics are often visualized in order give players more insight in their game progress. For instance, players can view their number of kills, the number of deaths, the average score per minute, their accuracy percentage, and how many games they have won. In addition, players can see which rank they have, how many hours they have played, and how many times a specific weapon is used. In addition, a visualized progress bar is shown which informs a player about what is completed and his current rank. These bars are usually associated with leveling systems. Furthermore, this progress bar is also visualized with a greyed-out tone and shows players how many percent of their points is earned, and how many is required to get a higher rank. Basically, all achievements, ranks and other game data aspects can be recorded and visualized on these platforms.

Figure 2: Win/Lose graph (screenshot taken from Battelog interface)

It is also worth noting that players not only use these systems to see their accomplished achievements, but can also give them insight “into their play behavior, and potentially get better at playing the game” (Medler 96). In addition, this insight may affect a player’s strategy, because it allows players “to review and plan their future game actions outside of real-time gameplay” (Medler 13). In this way, players can use these play analytic systems to optimize their strategy. This is also explained by Medler, who emphasizes that play analytic systems support play through game data visualizations.

Casual InfoVis

Interesting to note is that “whether play analytic systems are built by players or developers, the systems are always focused on the player as an audience” (Medler 12). From this perspective, the visualization of game data on these platforms can also be seen as a casual information visualization (Casual Infovis), a term that is introduced by Zachary Pousman. Pousman shows in his work on casual information visualization how users can analyze data from a casual approach. According to Pousman, casual infovis “is the use of computer mediated tools to depict personally meaningful information in visual ways that support everyday users in both everyday work and non-work situations” (Pousman 1149). He points out that casual Infovis is not only used in a working environment, rather it is more used for ‘casual use’. For instance, this can be found in software for visualizing personal data such as photo collections. Furthermore, Zachary Pousman explains that “visualizations of social processes, social networks, and social situations have become another emerging and exciting domain for infovis researchers” (1146). Another difference between traditional infovis systems and Casual Infovis systems is that a Casual Infovis is personally important. Furthermore, users of Casual Infovis may vary from experts to novices. They are not necessarily expert in analytic thinking, nor are they required to be experts at reading visualizations (Pousman 1149).To gain insight into a casual infovis system, Pousman uses the Nike+ Running app as an example. The Nike+ Running app tracks distance, pace, time and calories burned with GPS and provides the user with constant feedback. Just as in the example of Nike, platforms such as Battlelog and Xbox Live also visualize information that is personal in nature. For instance, these platforms visualize a player’s gameplay history and their gained achievements. In this way, players can view all sorts of stats tracking and visualizations of their gameplay data and achievements. In addition, the player does not need to be an expert to be able to analyze this type of information visualization.

Moreover, these platforms show similarities with the idea that “visualization may have a catalytic effect on communication between users” (Viegas 1). From this perspective, social information can be used for social purposes. When looking closely at Battlelog and Xbox Live, it becomes clear that players can view each other‘s profiles and see what games their friends are playing. In this respect, these platforms allow players to analyze and discuss their game data with each other. This casual and social approach of data analysis can give players more insight in their own game performance.


Medler, Ben. “Play with data-an exploration of play analytics and its effect on player experiences.” (2012).

Pousman, Zachary, John T. Stasko, and Michael Mateas, 2007. Casual Information Visualization: Depictions of Data in Everyday Life. IEEE Transactions on Visualization and Computer Graphics 13, 6, pp. 1145-1152.

Viegas, Fernanda, Martin Wattenberg, Matt McKeon, Frank van Ham, and Jesse Kriss, 2008. Harry Potter and the Meat-Filled Freezer: A Case Study of Spontaneous Usage of Visualization Tools. In Proceedings of the Proceedings of the 41st Annual Hawaii International Conference on System Sciences (HICSS ’08), IEEE Computer Society.

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